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The Silent Killer of GenAI Success: Lack of Unstructured Data Intelligence

Published July 2, 2024

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Take a look at the most recent news on technology, and you'll find more than half about AI. GenAI (Generative AI) in particular, is now taking the industry by storm. A recent Gartner survey shows GenAI is the most frequently deployed AI solution, acting as a catalyst for the expansion of AI in enterprises. This swift GenAI adoption has brought unstructured data into the limelight, highlighting the pivotal role it can play in driving innovation and growth.

Enterprises have traditionally leveraged structured data for business decisions, often ignoring unstructured data like text, images, videos, and audio, which lack an easily identifiable structure or predefined data model. GenAI models can analyze, interpret, and generate content from this data, estimated to be 90% of enterprise data created today. The ability to extract insights from unstructured data marks a significant shift, demanding a greater focus on utilizing it effectively.


Unstructured Data

For your GenAI projects to be successful, understanding and managing unstructured data is crucial. However, deriving meaningful insights from these chaotic data formats presents a challenge. Is the lack of unstructured data intelligence silently undermining GenAI efforts? A 2023 survey of data leaders highlights a critical gap. While enthusiasm for generative AI is high, readiness is lacking. Many organizations have not yet adapted their data strategies or data management practices to support GenAI effectively.

Why is Unstructured Data Intelligence Important?

Data intelligence involves understanding, analyzing, and interpreting information about data to extract meaningful insights for effective utilization. It uncovers details about data origins, classification, quality, ownerships, changes, and interconnections. In other words, it tells you the who, what, when, where, and how of data.

Unstructured data intelligence is particularly important in the GenAI era, to safely harness this valuable enterprise resource. Unstructured data intelligence is critical to:

  • Gain contextual insight into all your files, documents, and objects.
  • Automate data discovery and classification.
  • Govern and utilize all your proprietary enterprise data efficiently.
  • Execute your GenAI projects with confidence.
  • Enhance risk management and compliance.

The Challenges of Unstructured Data Intelligence

Unstructured data is characterized by no predefined data structure or organization. This is a huge obstacle to understanding and processing this data. When used for GenAI, a lack of intelligence on unstructured data can lead to poor or biased results, as well as data security, privacy, and compliance issues.

The road to Unstructured Data Intelligence is fraught with several challenges for enterprises, notably:

  1. Large Volumes: Enterprise data is exploding, with over 300 million terabytes of data generated every day in 2024. About 90% of this data is unstructured, putting substantial pressure on enterprises to interpret and manage it effectively.
  2. Diverse Formats: The lack of standardized formats affects data discovery and classification, making it difficult to use standard tools or universal frameworks.
  3. Varied Sources: Unstructured data can come from a wide variety of sources including news, chats, photos, research reports, podcasts, sensor data, and increasingly videos. Some of these sources may be unreliable, raising issues about the quality and value of the data.
  4. Resource-intensive Processing: The volume and variety of unstructured data demand robust infrastructure, significant computational power, and specialized tools processing.
  5. Real-time Processing: Gaining intelligence on unstructured data in real time for GenAI use cases requires high computational power and speed. It also demands advanced techniques like NLP and ML to build context and deliver deep, accurate, meaningful insights.
  6. Safeguarding Sensitive Information: Ensuring data privacy and security is critical, as unstructured data often contains hidden sensitive information. It is essential to balance effective processing and classification with stringent data protection measures.

Structured vs. Unstructured Data

Structured Data Unstructured Data
Just 10% of enterprise data. Estimated to be 90% of enterprise data.
Stored in tables, rows and columns, making it easier to process and manage. Stored in its native format and requires advanced techniques to harness.
Wide variety of tools available for data intelligence. Sophisticated tools are essential for discovery, cataloging, and classification.

How Securiti Delivers Unstructured Data Intelligence

Understanding unstructured data begins with identifying where it resides in the organizational silos. Then comes cataloging and classifying the uncovered data. With Securiti you can:

  • Discover shadow and cloud-native unstructured data assets across different sources and environments.
  • Catalog all files and objects that can be used in GenAI projects.
  • Classify data based on its type, sensitivity, relevance, and other criteria.
  • Document the data's characteristics, such as its metadata, ownership, and usage policies.

Securiti helps you gain contextual insights for unstructured data from all key perspectives, with a multidimensional Data Command Graph. A Data Command Graph is a Knowledge Graph that captures all key metadata and relationships between them.

  • Data Systems
  • Buckets / Folders
  • Files / Objects / Documents
  • Data Sensitivity
  • Access & Entitlements
  • Internal Policies & Controls
  • Applicable Regulations
  • GenAI Models / Pipelines

These insights serve as a baseline for the safe utilization of unstructured data for GenAI. You can uncover deep information for files of all types that include documents, images, audio, video, CLOBs, and many more. With Securiti Data Command Graph, you can get a complete view of:

  • File categories based on content, for example, legal, finance, or HR
  • Access and user entitlements
  • Sensitive objects within a file
  • Regulations applicable to file content
  • File quality, such as freshness, relevance, or uniqueness
  • Lineage of files and embeddings used in GenAI pipelines

6 Best Practices for Unstructured Data Intelligence

Safe and effective use of unstructured data is crucial for GenAI success. Here are six best practices to help you maximize its potential and achieve complete intelligence:

  1. Make unstructured data part of your enterprise data strategy: Integrate unstructured data into your overall data management plan to ensure it is effectively utilized for GenAI use cases. This approach helps in unlocking hidden insights and enhancing the decision-making processes.
  2. Define your data intelligence objectives for GenAI projects: Clearly outline how you want to use the data intelligence to power your GenAI projects, including the objectives and desired outcomes. This step will help you ensure that the extracted data intelligence aligns with your project and organizational goals.
  3. Invest in a comprehensive solution for both unstructured and structured data: Use integrated tools that handle both data types to streamline data processing and analysis. This approach optimizes resource use and improves overall data intelligence.
  4. Leverage Knowledge Graph for interconnected relationships: Knowledge Graph plays a crucial role in providing data intelligence, visibility, and governance within a GenAI pipeline. It enhances the efficiency, effectiveness, and security of data management and usage across the system.
  5. Use a unified platform for enabling data governance, security and privacy: Data Command Center, a purpose-built platform designed from the ground up, can address a broad range of your use cases across privacy, security, governance, and compliance. It enables you to break down silos and streamline collaboration across the organization by providing a single source of truth for data and AI intelligence.
  6. Promote Data Governance and Security for the safe use of unstructured data with GenAI: Implement robust data governance policies and security measures to ensure compliance and data integrity. This is crucial for safely using unstructured data, which comes from varied sources, often contains sensitive information, and resides in organizational silos.

In Summary

The rapid adoption of GenAI has highlighted the importance of unstructured data in driving innovation and growth. GenAI models feed on unstructured data—such as text, images, videos, and audio—to generate business insights, automate business processes, and build AI assistants. However, understanding and processing this data presents significant challenges and requires sophisticated tools to harness its full potential.

Learn how to gain comprehensive intelligence on unstructured data to unlock its potential for LLM training, tuning, RAG, and other GenAI use cases. Download the white paper Harnessing Unstructured Data for GenAI: A Primer for CDOs.

In our next blog, we will explore the need to reassess data quality for unstructured data in the GenAI era.

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